Serum metabolites predict response to angiotensin II receptor blockers in patients with diabetes mellitus

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Serum metabolites predict response to angiotensin II receptor blockers in patients with diabetes mellitus. / Pena, Michelle J; Heinzel, Andreas; Rossing, Peter; Parving, Hans-Henrik; Dallmann, Guido; Rossing, Kasper; Andersen, Steen; Mayer, Bernd; Heerspink, Hiddo J L.

I: Journal of Translational Medicine, Bind 14, 203, 05.07.2016.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

Harvard

Pena, MJ, Heinzel, A, Rossing, P, Parving, H-H, Dallmann, G, Rossing, K, Andersen, S, Mayer, B & Heerspink, HJL 2016, 'Serum metabolites predict response to angiotensin II receptor blockers in patients with diabetes mellitus', Journal of Translational Medicine, bind 14, 203. https://doi.org/10.1186/s12967-016-0960-3

APA

Pena, M. J., Heinzel, A., Rossing, P., Parving, H-H., Dallmann, G., Rossing, K., Andersen, S., Mayer, B., & Heerspink, H. J. L. (2016). Serum metabolites predict response to angiotensin II receptor blockers in patients with diabetes mellitus. Journal of Translational Medicine, 14, [203]. https://doi.org/10.1186/s12967-016-0960-3

Vancouver

Pena MJ, Heinzel A, Rossing P, Parving H-H, Dallmann G, Rossing K o.a. Serum metabolites predict response to angiotensin II receptor blockers in patients with diabetes mellitus. Journal of Translational Medicine. 2016 jul. 5;14. 203. https://doi.org/10.1186/s12967-016-0960-3

Author

Pena, Michelle J ; Heinzel, Andreas ; Rossing, Peter ; Parving, Hans-Henrik ; Dallmann, Guido ; Rossing, Kasper ; Andersen, Steen ; Mayer, Bernd ; Heerspink, Hiddo J L. / Serum metabolites predict response to angiotensin II receptor blockers in patients with diabetes mellitus. I: Journal of Translational Medicine. 2016 ; Bind 14.

Bibtex

@article{299f4ce07d66417ea9ab4d862cfe6da2,
title = "Serum metabolites predict response to angiotensin II receptor blockers in patients with diabetes mellitus",
abstract = "BACKGROUND: Individual patients show a large variability in albuminuria response to angiotensin receptor blockers (ARB). Identifying novel biomarkers that predict ARB response may help tailor therapy. We aimed to discover and validate a serum metabolite classifier that predicts albuminuria response to ARBs in patients with diabetes mellitus and micro- or macroalbuminuria.METHODS: Liquid chromatography-tandem mass spectrometry metabolomics was performed on serum samples. Data from patients with type 2 diabetes and microalbuminuria (n = 49) treated with irbesartan 300 mg/day were used for discovery. LASSO and ridge regression were performed to develop the classifier. Improvement in albuminuria response prediction was assessed by calculating differences in R(2) between a reference model of clinical parameters and a model with clinical parameters and the classifier. The classifier was externally validated in patients with type 1 diabetes and macroalbuminuria (n = 50) treated with losartan 100 mg/day. Molecular process analysis was performed to link metabolites to molecular mechanisms contributing to ARB response.RESULTS: In discovery, median change in urinary albumin excretion (UAE) was -42 % [Q1-Q3: -69 to -8]. The classifier, consisting of 21 metabolites, was significantly associated with UAE response to irbesartan (p < 0.001) and improved prediction of UAE response on top of the clinical reference model (R(2) increase from 0.10 to 0.70; p < 0.001). In external validation, median change in UAE was -43 % [Q1-Q35: -63 to -23]. The classifier improved prediction of UAE response to losartan (R(2) increase from 0.20 to 0.59; p < 0.001). Specifically ADMA impacting eNOS activity appears to be a relevant factor in ARB response.CONCLUSIONS: A serum metabolite classifier was discovered and externally validated to significantly improve prediction of albuminuria response to ARBs in diabetes mellitus.",
keywords = "Journal Article",
author = "Pena, {Michelle J} and Andreas Heinzel and Peter Rossing and Hans-Henrik Parving and Guido Dallmann and Kasper Rossing and Steen Andersen and Bernd Mayer and Heerspink, {Hiddo J L}",
year = "2016",
month = jul,
day = "5",
doi = "10.1186/s12967-016-0960-3",
language = "English",
volume = "14",
journal = "Journal of Translational Medicine",
issn = "1479-5876",
publisher = "BioMed Central",

}

RIS

TY - JOUR

T1 - Serum metabolites predict response to angiotensin II receptor blockers in patients with diabetes mellitus

AU - Pena, Michelle J

AU - Heinzel, Andreas

AU - Rossing, Peter

AU - Parving, Hans-Henrik

AU - Dallmann, Guido

AU - Rossing, Kasper

AU - Andersen, Steen

AU - Mayer, Bernd

AU - Heerspink, Hiddo J L

PY - 2016/7/5

Y1 - 2016/7/5

N2 - BACKGROUND: Individual patients show a large variability in albuminuria response to angiotensin receptor blockers (ARB). Identifying novel biomarkers that predict ARB response may help tailor therapy. We aimed to discover and validate a serum metabolite classifier that predicts albuminuria response to ARBs in patients with diabetes mellitus and micro- or macroalbuminuria.METHODS: Liquid chromatography-tandem mass spectrometry metabolomics was performed on serum samples. Data from patients with type 2 diabetes and microalbuminuria (n = 49) treated with irbesartan 300 mg/day were used for discovery. LASSO and ridge regression were performed to develop the classifier. Improvement in albuminuria response prediction was assessed by calculating differences in R(2) between a reference model of clinical parameters and a model with clinical parameters and the classifier. The classifier was externally validated in patients with type 1 diabetes and macroalbuminuria (n = 50) treated with losartan 100 mg/day. Molecular process analysis was performed to link metabolites to molecular mechanisms contributing to ARB response.RESULTS: In discovery, median change in urinary albumin excretion (UAE) was -42 % [Q1-Q3: -69 to -8]. The classifier, consisting of 21 metabolites, was significantly associated with UAE response to irbesartan (p < 0.001) and improved prediction of UAE response on top of the clinical reference model (R(2) increase from 0.10 to 0.70; p < 0.001). In external validation, median change in UAE was -43 % [Q1-Q35: -63 to -23]. The classifier improved prediction of UAE response to losartan (R(2) increase from 0.20 to 0.59; p < 0.001). Specifically ADMA impacting eNOS activity appears to be a relevant factor in ARB response.CONCLUSIONS: A serum metabolite classifier was discovered and externally validated to significantly improve prediction of albuminuria response to ARBs in diabetes mellitus.

AB - BACKGROUND: Individual patients show a large variability in albuminuria response to angiotensin receptor blockers (ARB). Identifying novel biomarkers that predict ARB response may help tailor therapy. We aimed to discover and validate a serum metabolite classifier that predicts albuminuria response to ARBs in patients with diabetes mellitus and micro- or macroalbuminuria.METHODS: Liquid chromatography-tandem mass spectrometry metabolomics was performed on serum samples. Data from patients with type 2 diabetes and microalbuminuria (n = 49) treated with irbesartan 300 mg/day were used for discovery. LASSO and ridge regression were performed to develop the classifier. Improvement in albuminuria response prediction was assessed by calculating differences in R(2) between a reference model of clinical parameters and a model with clinical parameters and the classifier. The classifier was externally validated in patients with type 1 diabetes and macroalbuminuria (n = 50) treated with losartan 100 mg/day. Molecular process analysis was performed to link metabolites to molecular mechanisms contributing to ARB response.RESULTS: In discovery, median change in urinary albumin excretion (UAE) was -42 % [Q1-Q3: -69 to -8]. The classifier, consisting of 21 metabolites, was significantly associated with UAE response to irbesartan (p < 0.001) and improved prediction of UAE response on top of the clinical reference model (R(2) increase from 0.10 to 0.70; p < 0.001). In external validation, median change in UAE was -43 % [Q1-Q35: -63 to -23]. The classifier improved prediction of UAE response to losartan (R(2) increase from 0.20 to 0.59; p < 0.001). Specifically ADMA impacting eNOS activity appears to be a relevant factor in ARB response.CONCLUSIONS: A serum metabolite classifier was discovered and externally validated to significantly improve prediction of albuminuria response to ARBs in diabetes mellitus.

KW - Journal Article

U2 - 10.1186/s12967-016-0960-3

DO - 10.1186/s12967-016-0960-3

M3 - Journal article

C2 - 27378474

VL - 14

JO - Journal of Translational Medicine

JF - Journal of Translational Medicine

SN - 1479-5876

M1 - 203

ER -

ID: 172818155